Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/Mikacr1138/claude-bug-bountyWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/mikacr1138/claude-bug-bounty/hunt)<a href="https://agentmods.dev/commands/mikacr1138/claude-bug-bounty/hunt"><img src="https://agentmods.dev/badge/commands/mikacr1138/claude-bug-bounty/hunt/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/mikacr1138/claude-bug-bounty/hunt"><img src="https://agentmods.dev/badge/commands/mikacr1138/claude-bug-bounty/hunt.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.01660 |
| Opus 5 | $0.00000 | $0.00830 |
| Sonnet 5 | $0.00000 | $0.00332 |
| Haiku 4.5 | $0.00000 | $0.00166 |
Grade D, and why
hunt scanned grade D with 3 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Sends data to an external URLmediumData exfiltration
A POST to an outside endpoint may be telemetry or may be exfiltration; either way the mod talks to somewhere, and you should know where.
curl "https://target.com/api/webhook" -d "{\"url\": \"$INTERACT_URL\"}" Cloud metadata endpointhighServer-side request forgery
One request to 169.254.169.254 can return temporary IAM credentials.
curl "https://target.com/api/image?url=http://169.254.169.254/latest/meta-data/iam/security-credentials/" Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -sI https://$TARGET | grep -iE "server|x-powered-by|x-aspnet|x-runtime|x-generator" How it starts
The opening of the file, as written. The whole thing — 197 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/hunt
Active vulnerability hunting on a target.
What This Does
- Reads program scope (in-scope assets, exclusions, payment behavior)
- Loads recon output from
recon/<target>/if available - Detects tech stack and maps to primary bug classes
- Runs targeted tests for the highest-ROI bug classes
- Documents findings with exact HTTP requests
Usage
/hunt target.com
/hunt target.com --vuln-class idor
/hunt target.com --vuln-class ssrf
/hunt target.com --vuln-class graphql
/hunt target.com --source-code (if repo is available)
Phase 1: Read Before Touching (15 min)
Read Program Scope
1. Go to program page (HackerOne/Bugcrowd/Intigriti)
2. Note ALL in-scope domains — only test these
3. Note ALL out-of-scope domains — never test these (Vienna: /advuew/* excluded!)
4. Note impact types accepted (some exclude "low" severity)
5. Check average bounty — signals program generosity
Read Disclosed Reports (Intel)
# HackerOne Hacktivity for this program:
# https://hackerone.com/TARGET_NAME/hacktivity
# Search by bug class:
# https://hackerone.com/hacktivity?querystring=TARGET_NAME+IDOR
# https://hackerone.com/hacktivity?querystring=TARGET_NAME+SSRF
# Extract from each report:
# 1. Which endpoint
# 2. Which bug class
# 3. What parameter
# 4. What check was missing
# 5. What they paid
Phase 2: Tech Stack Detection (2 min)
TARGET="target.com"
curl -sI https://$TARGET | grep -iE "server|x-powered-by|x-aspnet|x-runtime|x-generator"
# Stack → Primary bug class:
# Ruby on Rails → mass assignment, IDOR
# Django → IDOR (ModelViewSet), SSTI
# Flask → SSTI (render_template_string), SSRF
# Laravel → mass assignment, IDOR
# Express/Node → prototype pollution, path traversal
# Spring Boot → Actuator endpoints, SSTI
# Next.js → SSRF via Server Actions, open redirect
# GraphQL → introspection, IDOR via node(), auth bypass on mutations
Phase 3: Active Testing
IDOR Testing (highest ROI)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 197 lines · 0 tokens per session scan D 67952629f676
hunt is a command published in the GitHub repository Mikacr1138/claude-bug-bounty (2 stars, last pushed yesterday), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,660 tokens. A static security scan graded it D with 3 findings (sends data to an external url, cloud metadata endpoint, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other commands, from other repositories
sicario.assess
Assess the repository against SicarioSpec guardrails.
sicario.review
Review the active spec, plan, tasks, staged changes, or branch for security and governance gaps.
triage-release-check
Triage a failed make release-check — read the captured log, identify the failing gate stage, and recommend the producer-side fix. Analysis only; applies nothing unless asked.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.